Conventional methods for assessing a patient's clinical outcome are primarily based on clinicians' judgment and past experience. The conventional methods generally involve laboratory tests, patient surveys and office visits at isolated time points, all of which are not scalable for a time series analysis, especially for one that tracks or predicts the trend of a patient's clinical outcome in real time. Intrinsic to the conventional methodologies is the profound drawback that a relatively small set of information such as a single clinician's personal preference is taken into consideration in reaching a clinical decision. As such, under the existing medical system, patient care becomes increasingly difficult when multiple variables are involved. In particular, there lacks a system and method to effect a multi-dimensional analysis in which a large set of biomarkers are used to aid in the diagnosis, prognosis, and treatment of a clinical outcome or the design and execution of a clinical trial.
Multivariate statistics are generally concerned with determining a statistical distribution of an outcome or a series of outcomes based on multiple variables. Inherently, most medical conditions and treatments are multivariate due to the complexities of the physiology. The discoveries of a vast number of disease biomarkers and the establishment of miniaturized analytic systems have made a new paradigm of patient care that makes multivariate analysis feasible. A desirable new paradigm would provide rapid access to information characterizing clinical outcome and then automatically linking that information through customized communication channels so that the desired medical actions (adaptive dose ranging, clinical decision making and so forth) can be performed. Also desirable is the ability to integrate information from an individual's blood tests with other physiologically relevant factors, and present that information in an actionable format. The technology described herein satisfies these needs and provides related advantages as well.